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STRING v10: protein–protein interaction networks, integrated over the tree of life

The many functional partnerships and interactions that occur between proteins are at the core of cellular processing and their systematic characterization helps to provide context in molecular systems biology. However, known and predicted interactions are scattered over multiple resources, and the a...

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Autores principales: Szklarczyk, Damian, Franceschini, Andrea, Wyder, Stefan, Forslund, Kristoffer, Heller, Davide, Huerta-Cepas, Jaime, Simonovic, Milan, Roth, Alexander, Santos, Alberto, Tsafou, Kalliopi P., Kuhn, Michael, Bork, Peer, Jensen, Lars J., von Mering, Christian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4383874/
https://www.ncbi.nlm.nih.gov/pubmed/25352553
http://dx.doi.org/10.1093/nar/gku1003
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author Szklarczyk, Damian
Franceschini, Andrea
Wyder, Stefan
Forslund, Kristoffer
Heller, Davide
Huerta-Cepas, Jaime
Simonovic, Milan
Roth, Alexander
Santos, Alberto
Tsafou, Kalliopi P.
Kuhn, Michael
Bork, Peer
Jensen, Lars J.
von Mering, Christian
author_facet Szklarczyk, Damian
Franceschini, Andrea
Wyder, Stefan
Forslund, Kristoffer
Heller, Davide
Huerta-Cepas, Jaime
Simonovic, Milan
Roth, Alexander
Santos, Alberto
Tsafou, Kalliopi P.
Kuhn, Michael
Bork, Peer
Jensen, Lars J.
von Mering, Christian
author_sort Szklarczyk, Damian
collection PubMed
description The many functional partnerships and interactions that occur between proteins are at the core of cellular processing and their systematic characterization helps to provide context in molecular systems biology. However, known and predicted interactions are scattered over multiple resources, and the available data exhibit notable differences in terms of quality and completeness. The STRING database (http://string-db.org) aims to provide a critical assessment and integration of protein–protein interactions, including direct (physical) as well as indirect (functional) associations. The new version 10.0 of STRING covers more than 2000 organisms, which has necessitated novel, scalable algorithms for transferring interaction information between organisms. For this purpose, we have introduced hierarchical and self-consistent orthology annotations for all interacting proteins, grouping the proteins into families at various levels of phylogenetic resolution. Further improvements in version 10.0 include a completely redesigned prediction pipeline for inferring protein–protein associations from co-expression data, an API interface for the R computing environment and improved statistical analysis for enrichment tests in user-provided networks.
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spelling pubmed-43838742015-04-08 STRING v10: protein–protein interaction networks, integrated over the tree of life Szklarczyk, Damian Franceschini, Andrea Wyder, Stefan Forslund, Kristoffer Heller, Davide Huerta-Cepas, Jaime Simonovic, Milan Roth, Alexander Santos, Alberto Tsafou, Kalliopi P. Kuhn, Michael Bork, Peer Jensen, Lars J. von Mering, Christian Nucleic Acids Res Database Issue The many functional partnerships and interactions that occur between proteins are at the core of cellular processing and their systematic characterization helps to provide context in molecular systems biology. However, known and predicted interactions are scattered over multiple resources, and the available data exhibit notable differences in terms of quality and completeness. The STRING database (http://string-db.org) aims to provide a critical assessment and integration of protein–protein interactions, including direct (physical) as well as indirect (functional) associations. The new version 10.0 of STRING covers more than 2000 organisms, which has necessitated novel, scalable algorithms for transferring interaction information between organisms. For this purpose, we have introduced hierarchical and self-consistent orthology annotations for all interacting proteins, grouping the proteins into families at various levels of phylogenetic resolution. Further improvements in version 10.0 include a completely redesigned prediction pipeline for inferring protein–protein associations from co-expression data, an API interface for the R computing environment and improved statistical analysis for enrichment tests in user-provided networks. Oxford University Press 2014-10-28 2015-01-28 /pmc/articles/PMC4383874/ /pubmed/25352553 http://dx.doi.org/10.1093/nar/gku1003 Text en © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Database Issue
Szklarczyk, Damian
Franceschini, Andrea
Wyder, Stefan
Forslund, Kristoffer
Heller, Davide
Huerta-Cepas, Jaime
Simonovic, Milan
Roth, Alexander
Santos, Alberto
Tsafou, Kalliopi P.
Kuhn, Michael
Bork, Peer
Jensen, Lars J.
von Mering, Christian
STRING v10: protein–protein interaction networks, integrated over the tree of life
title STRING v10: protein–protein interaction networks, integrated over the tree of life
title_full STRING v10: protein–protein interaction networks, integrated over the tree of life
title_fullStr STRING v10: protein–protein interaction networks, integrated over the tree of life
title_full_unstemmed STRING v10: protein–protein interaction networks, integrated over the tree of life
title_short STRING v10: protein–protein interaction networks, integrated over the tree of life
title_sort string v10: protein–protein interaction networks, integrated over the tree of life
topic Database Issue
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4383874/
https://www.ncbi.nlm.nih.gov/pubmed/25352553
http://dx.doi.org/10.1093/nar/gku1003
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